Prompting is not magic wording. It is specification: you reduce ambiguity and increase constraints so the model has less room to guess.
The 5-part prompt formula
- Role: who the assistant should behave as (copywriter, recruiter, QA lead).
- Objective: what success looks like (increase demo requests, reduce churn).
- Context: audience, product, industry, situation, examples.
- Constraints: length, tone, structure, must-include, must-avoid.
- Verification: assumptions, risks, and a checklist.
Good constraints that improve output quality
- Format: “Use H2 sections + bullets + a final checklist.”
- Length: “120–160 words” or “3 bullets max.”
- Audience language: “8th grade reading level” or “industry jargon OK.”
- Grounding: “If you don’t know, say so and ask questions.”
Example prompt
Act as a conversion copywriter.
Objective: write a landing page hero section that increases demo requests.
Context: B2B scheduling software for small clinics. Audience: office managers.
Constraints: 1 headline (<= 9 words), 1 subhead (<= 22 words), 3 bullets, 1 CTA.
Verification: list 3 assumptions and 3 risks.
Iteration workflow
- Generate v1 quickly.
- Pick the best parts (headline, angle, structure).
- Re-run with stronger constraints and “keep these parts.”
- Finalize and verify claims.
Next: Common Prompt Mistakes and Verification Checklist.
FAQ
What is a good prompt structure?
A good prompt includes a role, objective, context, constraints, and a verification step (assumptions + checklist).
How long should a prompt be?
As long as needed to remove ambiguity. Short prompts can work if the task is simple; complex tasks benefit from explicit constraints and examples.
How do I stop generic outputs?
Add audience specifics, objections, examples, and formatting constraints. Also ask for two variants and a critique pass.
Should I ask the model to cite sources?
For factual claims, yes—ask for sources or a list of statements that require verification, then confirm independently.